{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 第5章 支持向量机\n",
    ">- 支持向量机（简称SVM）是一个功能强大并且全面的机器学习模型，\n",
    ">- 它能够执行线性或非线性分类、回归，甚至是异常值检测任务。\n",
    ">- 它是机器学习领域最受欢迎的模型之一，任何对机器学习感兴趣的人都应该在工具箱中配备一个。\n",
    ">- SVM特别适用于中小型复杂数据集的分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# 线性SVM分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## SVM概念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 将SVM分类器视为在类别之间拟合可能的最宽的街道（平行的虚线所示） 。 因此这也叫作大间隔分类（large margin classification）\n",
    "\n",
    "![](img/5-1.png)\n",
    "\n",
    "- 请注意，在街道以外的地方增加更多训练实例，不会对决策边界产生影响：也就是说它完全由位于街道边缘的实例所决定（或者称之为“支持”）。这些实例被称为支持向量\n",
    "\n",
    "- SVM对特征的缩放非常敏感，如图5-2所示，在左图中，垂直刻度比水平刻度大得多，因此可能的最宽的街道接近于水平。在特征缩放（例如使用Scikit-Learn的StandardScaler）后，决策边界看起来好很多（见右图）。\n",
    "\n",
    "![](img/5-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### 软间隔分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 硬间隔分类\n",
    "- 如果我们严格地让所有实例都不在街道上， 并且位于正确的一边，这就是硬间隔分类。\n",
    "- 硬间隔分类有两个主要问题，\n",
    "- 首先，它只在数据是线性可分离的时候才有效；\n",
    "- 其次，它对异常值非常敏感。\n",
    "\n",
    "![](img/5-3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 软间隔分类\n",
    "- 要避免这些问题， 最好使用更灵活的模型。\n",
    "- 目标是尽可能在保持街道宽阔和限制间隔违例（即位于街道之上，甚至在错误的一边的实例）之间找到良好的平衡，这就是软间隔分类。\n",
    "- 在Scikit-Learn的SVM类中，可以通过超参数C来控制这个平衡：C值越小，则街道越宽，但是间隔违例也会越多\n",
    ">C所代表的是惩罚因子\n",
    "\n",
    "![](img/5-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 代码实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X = iris['data'][:, (2, 3)]    # 只取第三第四个特征\n",
    "y = (iris['target'] == 2)      # 判断是否为第三种花"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_clf = Pipeline((\n",
    "    ('scaler', StandardScaler()),     # 标准化\n",
    "    ('linear_svc', LinearSVC(C=1, loss='hinge')),   # 线性svm模型\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('linear_svc',\n",
       "                 LinearSVC(C=1, class_weight=None, dual=True,\n",
       "                           fit_intercept=True, intercept_scaling=1,\n",
       "                           loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "                           penalty='l2', random_state=None, tol=0.0001,\n",
       "                           verbose=0))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5,1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- LinearSVC类会对偏置项进行正则化， 所以你需要先减去平均值， 使训练集集中。\n",
    "- 如果使用StandardScaler会自动进行这一步。\n",
    "- 此外，请确保超参数loss设置为\"hinge\"，因为它不是默认值。\n",
    "- 最后，为了获得更好的性能，还应该将超参数dual设置为False， \n",
    "- 除非特征数量比训练实例还多（本章后文将会讨论）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# 非线性SVM分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 引入\n",
    "- 虽然在许多情况下，线性SVM分类器是有效的，并且通常出人意料的好，\n",
    "- 但是，有很多数据集远不是线性可分离的。\n",
    "- 处理非线性数据集的方法之一是添加更多特征，\n",
    "- 比如多项式特征（如第4章所述），某些情况下，这可能导致数据集变得线性可分离。\n",
    "\n",
    "![](img/5-5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 代码示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_datasets(X, y, axse):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "    plt.xlim(axse[0], axse[1])\n",
    "    plt.ylim(axse[2], axse[3])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1.0, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Davion\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('poly_features',\n",
       "                 PolynomialFeatures(degree=3, include_bias=True,\n",
       "                                    interaction_only=False, order='C')),\n",
       "                ('sacler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svm_clf',\n",
       "                 LinearSVC(C=10, class_weight=None, dual=True,\n",
       "                           fit_intercept=True, intercept_scaling=1,\n",
       "                           loss='hinge', max_iter=1000, multi_class='ovr',\n",
       "                           penalty='l2', random_state=42, tol=0.0001,\n",
       "                           verbose=0))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial_svm_clf = Pipeline((\n",
    "    ('poly_features', PolynomialFeatures(degree=3)),  # 添加多项式特征\n",
    "    ('sacler', StandardScaler()),       # 标准化\n",
    "    ('svm_clf', LinearSVC(C=10, loss='hinge', random_state=42)),  # svm模型\n",
    "))\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial_svm_clf.predict([[1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多项式核"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 添加多项式特征实现起来非常简单， 并且对所有的机器学习算法（不只是SVM） 都非常有效。\n",
    "- 但问题是， 如果多项式太低阶， 处理不了非常复杂的数据集，\n",
    "- 而高阶则会创造出大量的特征，导致模型变得太慢。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有一个魔术般的数学技巧可以应用，这就是核技巧。\n",
    "- 它产生的结果就跟添加了许多多项式特征，\n",
    "- 甚至是非常高阶的多项式特征一样，但实际上并不需要真的添加。\n",
    "- 因为实际没有添加任何特征， 所以也就不存在数量爆炸的组合特征了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多项式核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svm_clf',\n",
       "                 SVC(C=5, cache_size=200, class_weight=None, coef0=1,\n",
       "                     decision_function_shape='ovr', degree=3,\n",
       "                     gamma='auto_deprecated', kernel='poly', max_iter=-1,\n",
       "                     probability=False, random_state=None, shrinking=True,\n",
       "                     tol=0.001, verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "poly_kernel_svm_clf = Pipeline((\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='poly', degree=3, coef0=1, C=5)),\n",
    "))\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svm_clf',\n",
       "                 SVC(C=5, cache_size=200, class_weight=None, coef0=100,\n",
       "                     decision_function_shape='ovr', degree=10,\n",
       "                     gamma='auto_deprecated', kernel='poly', max_iter=-1,\n",
       "                     probability=False, random_state=None, shrinking=True,\n",
       "                     tol=0.001, verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_kernel_svm_clf_2 = Pipeline((\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='poly', degree=10, coef0=100, C=5)),\n",
    "))\n",
    "poly_kernel_svm_clf_2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'degree=10, coef0=100, C=5')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title('degree=3, coef0=1, C=5')\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly_kernel_svm_clf_2, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title('degree=10, coef0=100, C=5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<small>超参数coef0控制的是模型受高阶多项式还是低阶多项式影响的程度</small>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 高斯核"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 解决非线性问题的另一种技术是添加相似特征。\n",
    "- 这些特征经过相似函数计算得出，\n",
    "- 相似函数可以测量每个实例与一个特定地标（landmark）之间的相似度。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 高斯RBF\n",
    "$$ \\phi\\gamma(x, l)=exp(-\\gamma||x-l||^2) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 这是一个从0（离地标差得非常远） 到1（跟地标一样） 变化的钟形函数。\n",
    "- 现在我们准备计算新特征。 \n",
    "- 例如， 我们看实例x1=-1： 它与第一个地标的距离为1， 与第二个地标的距离为2。\n",
    "- 因此它的新特征为x2=eps（-0.3×12） ≈0.74， x3=eps（-0.3×22） ≈0.30。\n",
    "- 图5-8的右图显示了转换后的数据集（去除了原始特征），\n",
    "- 现在你可以看出， 数据呈线性可分离的了。\n",
    "\n",
    "![](img/5-8.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 高斯RBF核函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svm_clf',\n",
       "                 SVC(C=0.001, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=5,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline((\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='rbf', gamma=5, C=0.001))\n",
    "))\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(rbf_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svm_clf',\n",
       "                 SVC(C=1000, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=5,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf_1 = Pipeline((\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='rbf', gamma=0.1, C=0.001))\n",
    "))\n",
    "rbf_kernel_svm_clf_1.fit(X, y)\n",
    "\n",
    "rbf_kernel_svm_clf_2 = Pipeline((\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='rbf', gamma=0.1, C=1000))\n",
    "))\n",
    "rbf_kernel_svm_clf_2.fit(X, y)\n",
    "\n",
    "rbf_kernel_svm_clf_3 = Pipeline((\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='rbf', gamma=5, C=0.001))\n",
    "))\n",
    "rbf_kernel_svm_clf_3.fit(X, y)\n",
    "\n",
    "rbf_kernel_svm_clf_4 = Pipeline((\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svm_clf', SVC(kernel='rbf', gamma=5, C=1000))\n",
    "))\n",
    "rbf_kernel_svm_clf_4.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'gamma=5, C=1000')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 8))\n",
    "\n",
    "plt.subplot(221)\n",
    "plot_predictions(rbf_kernel_svm_clf_1, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title('gamma=0.1, C=0.001')\n",
    "\n",
    "plt.subplot(222)\n",
    "plot_predictions(rbf_kernel_svm_clf_2, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title('gamma=0.1, C=1000')\n",
    "\n",
    "plt.subplot(223)\n",
    "plot_predictions(rbf_kernel_svm_clf_3, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title('gamma=5, C=0.001')\n",
    "\n",
    "plt.subplot(224)\n",
    "plot_predictions(rbf_kernel_svm_clf_4, [-1.5, 2.5, -1, 1.5])\n",
    "plot_datasets(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title('gamma=5, C=1000')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 有这么多的核函数， 该如何决定使用哪一个呢？\n",
    "- 有一个经验法则是，\n",
    "- 永远先从线性核函数开始尝试（要记住， LinearSVC比SVC（kernel=\"linear\"） 快得多），特别是训练集非常大或特征非常多的时候。\n",
    "- 如果训练集不太大， 你可以试试高斯RBF核， 大多数情况下它都非常好用。\n",
    "- 如果你还有多余的时间和计算能力， 你可以使用交叉验证和网格搜索来尝试一些其他的核函数，\n",
    "- 特别是那些专门针对你的数据集数据结构的核函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算复杂度\n",
    "![](img/5-1-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# SVM回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 概念\n",
    "- 正如前面提到的，SVM算法非常全面：它不仅支持线性和非线性分类，而且还支持线性和非线性回归。\n",
    "- 诀窍在于将目标反转一下：\n",
    "- 不再是尝试拟合两个类别之间可能的最宽的街道的同时限制间隔违例，\n",
    "- SVM回归要做的是让尽可能多的实例位于街道上， 同时限制间隔违例（也就是不在街道上的实例）。街道的宽度由超参数ε控制。\n",
    "- 图5-10显示了用随机线性数据训练的两个线性SVM回归模型， 一个间隔较大（ε＝1.5） ， 另一个间隔较小（ε＝0.5）\n",
    "\n",
    "![](img/5-10.png)\n",
    "\n",
    "- 在间隔内添加更多的实例不会影响模型的预测， 所以这个模型被称为ε不敏感。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里生成左边的图\n",
    "from sklearn.svm import LinearSVR\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(C=1.0, dual=True, epsilon=1.5, fit_intercept=True,\n",
       "          intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
       "          random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svm_reg(model, xlim):\n",
    "    X_new = np.linspace(xlim[0], xlim[1], 100).reshape(100, -1)\n",
    "    y_new_pred = svm_poly_reg.predict(X_new)\n",
    "    plt.plot(X, y, '.')\n",
    "    plt.plot(X_new, y_new_pred, 'k-')\n",
    "    plt.plot(X_new, y_new_pred+svm_poly_reg.epsilon, '--')\n",
    "    plt.plot(X_new, y_new_pred-svm_poly_reg.epsilon, '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svm_reg(svm_reg, [0,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 代码示例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='auto',\n",
       "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel='poly', degree=2, C=100, epsilon=0.1, gamma='auto')\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svm_reg(svm_poly_reg, [-1, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作原理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策函数和预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 线性SVM分类器通过简单地计算决策函数$w^tx+b=w_1x_1+……+w_nx_n$来预测新实例x的分类。如果结果为正，则预测类别是正类（1），不然则预测其为负类（0） "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 线性SVM分类器预测\n",
    "$$ \\hat{y}=\\begin{cases}\n",
    "0 & w^tx+b<0\\\\\n",
    "1 & w^tx+b>=0\n",
    "\\end{cases} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/5-12.png)\n",
    "\n",
    "- 数据集包含两个特征（花瓣宽度和长度），所以是一个二维平面。决策边界是决策函数等于0的点的合：它是两个平面的交集，也就是一条直线（加粗实线所示）\n",
    "- 虚线表示决策函数等于1或-1的点：它们互相平行，并且与决策边界的距离相等，从而形成了一个间隔。训练线性SVM分类器即意味着找到w和b的值，从而使这个间隔尽可能宽的同时，避免（硬间隔）或是限制（软间隔）间隔违例。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练目标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 思考一下决策函数的斜率：\n",
    "- 它等于权重向量的范数， 即||w||。\n",
    "- 如果我们将斜率除以2， 那么决策函数等于±1的点也将变得离决策函数两倍远。\n",
    "- 也就是说， 将斜率除以2， 将会使间隔乘以2。\n",
    "- 也许2D图更容易将其可视化，权重向量w越小， 间隔越大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2, 2)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3))\n",
    "plt.subplot(121)\n",
    "X_temp = np.linspace(-3, 3, 1000)\n",
    "plt.plot(X_temp, 1 * X_temp)\n",
    "plt.xlim(-3,3)\n",
    "plt.ylim(-2,2)\n",
    "\n",
    "plt.subplot(122)\n",
    "X_temp = np.linspace(-3, 3, 1000)\n",
    "plt.plot(X_temp, 0.5 * X_temp)\n",
    "plt.xlim(-3,3)\n",
    "plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 硬间隔线性SVM分类器的目标\n",
    "\n",
    "![](img/5-12-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 软间隔线性SVM分类器目标\n",
    "要达到软间隔的目标， 我们需要为每个实例引入一个松弛变量\n",
    "ζ（i） ≥0\n",
    "\n",
    "![](img/5-12-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二次规划\n",
    "![](img/5-12-3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
